基于VMD-PSR-BNN模型的月径流预测方法研究

Research on the Monthly Runoff Prediction Method Based on VMD-PSR-BNN Model

径流过程是地球上水文循环中的关键一环,科学准确地预测月径流的来水量对于流域的水量调度、水资源规划及管理具有十分重要的意义。然而由于径流过程的复杂性以及人类活动的影响,在变化环境中精准捕捉月径流时间序列的变化规律变得十分困难。针对月径流时间序列预测中存在的对于样本数据中先验信息识别不够彻底以及对时间步长嵌入维度难以有效地自适应选取这两点问题,设计了基于VMD-PSR-BNN的月径流时间序列预测模型。基于变分模态分解(VMD)算法对噪声良好的鲁棒性和对时序信号精确分解的特性,将月径流时间序列视为一种时序信号,利用VMD方法将月径流时间序列分解为多个相对平稳的固有模态函数(IMF),再基于相空间重构(PSR)理论对各个IMF分别进行重构,对各个重构后的IMF分别采用基于变分推理的贝叶斯神经网络(BNN)进行预测,最后将各个BNN的预测结果进行聚合重构得到月径流时间序列的最终预测结果。选取渭河流域咸阳和华县两个水文站1953-2018年的月径流时间序列进行实例分析。结果表明:VMD对月径流时间序列具有很好的分解效果,两个水文站基于VMD-PSR-BNN模型的月径流预测结果均可达到水文预报的甲级标准,并且对于样本中的极端值具有较好的拟合效果,为月径流时间序列的预测提供了新的方法参考。

The runoff process is a vital part of the earth’s hydrological cycle.Scientific and accurate prediction of monthly runoff inflow is of great significance for water flow scheduling,water resources planning and management in the basin.However,due to the complexity of the runoff process and the influence of human activities,it is very difficult to accurately capture the variation law of the monthly runoff time se⁃ries in a changing environment.Because of the two problems in the prediction of monthly runoff time series,the prior information identifica⁃tion in the sample data is not thorough enough,and the time step embedding dimension is difficult to be effectively and adaptively selected.This paper designs a model for monthly runoff time series based on VMD-PSR-BNN.Based on the good robustness of variational mode decomposition(VMD)algorithm to noise and the characteristics of accurate decomposition of time series signals,the monthly runoff time series is regarded as a time series signal,and the VMD method is used to decompose the monthly runoff time series into multiple relatively station⁃ary intrinsic mode function(IMF).Then,each IMF is reconstructed based on the phase space reconstruction(PSR)theory,and the Bayes⁃ian neural network(BNN)based on variational inference is used to predict each reconstructed IMF.Finally,the prediction results of each BNN are aggregated and reconstructed to obtain the final prediction result.The monthly runoff time series from 1953 to 2018 of two hydrologi⁃cal stations in Xianyang and Huaxian in the Weihe River Basin are selected for case analysis.The results show that the prediction results of the two hydrological stations based on the VMD-PSR-BNN model can reach the first-class standard of hydrological forecasting,and have a good fitting effect for the extreme values in the sample,which provides a new method reference for the prediction of monthly runoff time series.